library(NNbenchmark)
library(ANN2)

library(stringr)
library(dplyr)
library(kableExtra)

Set Environment

options(scipen = 9999)
options("digits.secs" = 2)
timer  <- createTimer(verbose = FALSE)

Datasets to Test

NNdataSummary(NNdatasets)

NN Train Function

hyperParams <- function(optim_method) {
    
    if (!is.element(optim_method, c("sgd", "adam", "rmsprop"))) stop("Invalid Parameters.")
    if (optim_method == "sgd") { iter <- 10; lr <- 0.01; params <- paste0("method=", optim_method, "_", "lr=", lr, "_", "iter=", iter)} 
    if (optim_method == "adam") { iter <- 15; lr <- 0.02; params <- paste0("method=", optim_method, "_", "lr=", lr, "_", "iter=", iter)} 
    if (optim_method == "rmsprop") { iter <- 20; lr <- 0.03; params <- paste0("method=", optim_method, "_", "lr=", lr, "_", "iter=", iter)} 
    
    params <- paste0("method=", optim_method, "_", "lr=", lr, "_", "iter=", iter)
    
    out <- list(iter = iter, lr = lr, params = params)
    
    return (out)
}



NNtrain <- function(x, y, hidden_neur, optim_method) {
    
    hyper_params <- hyperParams(optim_method)
    
    iter <- hyper_params$iter
    lr <- hyper_params$lr
    
    NNreg <- neuralnetwork(X = x, y = y, 
                           val.prop = 0, 
                           standardize = FALSE, 
                           hidden.layers = c(hidden_neur), 
                           regression = TRUE,
                           loss.type = "squared",
                           n.epochs = iter,
                           optim.type = optim_method,
                           learn.rates = lr,
                           verbose = FALSE,
                           random.seed = as.integer(runif(1)*10000000))
    
    return (NNreg)
}

Main Loop

for (dset in names(NNdatasets)) {

    ## =============================================
    ## EXTRACT INFORMATION FROM THE SELECTED DATASET
    ## =============================================
    ds     <- NNdatasets[[dset]]$ds
    Z      <- NNdatasets[[dset]]$Z
    neur   <- NNdatasets[[dset]]$neur
    nparNN <- NNdatasets[[dset]]$nparNN
    fmlaNN <- NNdatasets[[dset]]$fmlaNN
    donotremove  <- c("dset", "dsets", "ds", "Z", "neur", "TF", "nrep", "timer",
                      "donotremove", "donotremove2")
    donotremove2 <- c("dset", "dsets") 

    ## ===================================================
    ## SELECT THE FORMAT REQUIRED BY THE PACKAGE/ALGORITHM
    ## d = data.frame, m = matrix, v = vector/numeric
    ## ATTACH THE OBJECTS CREATED (x, y, Zxy, ... )
    ## ===================================================
    ZZ     <- prepareZZ(Z, xdmv = "d", ydmv = "v", zdm = "d", scale = TRUE)
    attach(ZZ)

    ## =============================================
    ## SELECT THE PACKAGE USED FOR TRAINING
    ## nrep => SELECT THE NUMBER OF INDEPENDANT RUNS
    ## iter => SELECT THE MAX NUMBER OF ITERATIONS
    ## TF   => PLOT THE RESULTS
    ## =============================================

    
    nrep   <- 5
    TF     <- TRUE 

    method <- c("sgd", "adam", "rmsprop")
        
    for (m in method) {
        
        descr  <- paste(dset, "ANN2::neuralnetwork", m, sep = "_")

        ## AUTO
        Ypred  <- list()
        Rmse   <- numeric(length = nrep)
        Mae    <- numeric(length = nrep)
    
        for(i in 1:nrep){
            event      <- paste0(descr, sprintf("_%.2d", i))
            timer$start(event)
            #### ADJUST THE FOLLOWING LINES TO THE PACKAGE::ALGORITHM
            
            hyper_params <- hyperParams(optim_method = m)

            NNreg      <- tryCatch(
                            NNtrain(x = x, y = y, hidden_neur = neur, optim_method = m),
                            error = function(y) {lm(y ~ 0, data = Zxy)}
                          )     
            y_pred     <- tryCatch(
                            ym0 + ysd0*predict(NNreg, x)$predictions,
                            error = ym0
                          )     
            ####
            Ypred[[i]] <- y_pred
            Rmse[i]    <- funRMSE(y_pred, y0)
            Mae[i]     <- funMAE(y_pred, y0)
            timer$stop(event, RMSE = Rmse[i], MAE = Mae[i], params = hyper_params$params, printmsg = FALSE)
        }
        best <- which(Rmse == min(Rmse, na.rm = TRUE))[1]
        best ; Rmse[[best]]
        
        ## ================================================
        ## PLOT ALL MODELS AND THE MODEL WITH THE BEST RMSE
        ## par OPTIONS CAN BE IMPROVED FOR A BETTER DISPLAY
        ## ================================================
        op <- par(mfcol = c(1,2))
        plotNN(xory, y0, uni, TF, main = descr)
        for (i in 1:nrep) lipoNN(xory, Ypred[[i]], uni, TF, col = i, lwd = 1)
        
        plotNN(xory, y0, uni, TF, main = descr)
        lipoNN(xory, Ypred[[best]], uni, TF, col = 4, lwd = 4)
        par(op)
    }


## ===========================
## DETACH ZZ - END OF THE LOOP
## ===========================
    detach(ZZ)
}

Results

dfr0 <- getTimer(timer) 

dfr  <- data.frame(
    ds_pkg.fun_algo = stringr::str_sub(dfr0[ ,1], 1, -4),
    run     = stringr::str_sub(dfr0[ ,1], -2, -1),
    dataset = stringr::str_replace_all(stringr::str_extract(dfr0[, 1], pattern = "^\\w*_"), fixed("_"), ""),
    method = stringr::str_replace_all(stringr::str_extract(dfr0[, 1], pattern = "_\\w*_"), fixed("_"), ""),
    Elapsed = round(dfr0[ ,4], 5),
    params = dfr0$params,
    dfr0[, c("RMSE","MAE")]
)


dfr

Best Results

dfr %>%
    group_by(dataset, method) %>%
    summarise(minRMSE = min(RMSE), meanRMSE = mean(RMSE), meanTime = mean(Elapsed)) %>%
    kable() %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
    collapse_rows(columns = 1:2, valign = "top")
dataset method minRMSE meanRMSE meanTime
mDette adam 3.0948 3.97626 0.008
rmsprop 2.8776 3.36848 0.010
sgd 4.9184 6.50956 0.028
mFriedman adam 0.0808 0.11418 0.012
rmsprop 0.0591 0.09248 0.010
sgd 0.1261 0.13998 0.010
mIshigami adam 2.7179 2.76180 0.014
rmsprop 2.3168 2.67236 0.020
sgd 2.9226 3.07040 0.008
mRef153 adam 6.0301 7.10828 0.010
rmsprop 4.6908 5.95992 0.006
sgd 7.5511 8.13686 0.006
uDmod1 adam 0.5276 0.57870 0.006
rmsprop 0.4809 0.51358 0.008
sgd 0.5673 0.69250 0.006
uDmod2 adam 0.3298 0.48506 0.004
rmsprop 0.3885 0.44276 0.006
sgd 0.4324 0.48176 0.010
uDreyfus1 adam 0.6140 0.68476 0.004
rmsprop 0.4876 0.59500 0.000
sgd 0.6968 0.80534 0.002
uDreyfus2 adam 0.6809 0.90494 0.004
rmsprop 0.4592 0.52160 0.000
sgd 0.6762 0.73370 0.004
uGauss1 adam 26.7491 27.76072 0.010
rmsprop 22.7846 25.61874 0.004
sgd 27.5268 28.25596 0.004
uGauss2 adam 19.2962 21.46538 0.008
rmsprop 13.2070 14.95164 0.016
sgd 17.3750 23.07154 0.002
uGauss3 adam 17.2794 20.72808 0.004
rmsprop 12.5749 14.10714 0.004
sgd 23.3915 27.76456 0.006
uNeuroOne adam 0.9157 1.02970 0.002
rmsprop 0.8519 0.95704 0.004
sgd 0.9877 1.09072 0.002
clearNN(donotremove)
---
title: "NNbenchmark | ANN2"
author: Akshaj Verma
output: html_notebook
---


```{r message=FALSE, warning=FALSE}
library(NNbenchmark)
library(ANN2)

library(stringr)
library(dplyr)
library(kableExtra)
```

## Set Environment
```{r}
options(scipen = 9999)
options("digits.secs" = 2)
timer  <- createTimer(verbose = FALSE)
```

## Datasets to Test

```{r}
NNdataSummary(NNdatasets)
```

## NN Train Function
```{r}
hyperParams <- function(optim_method) {
    
    if (!is.element(optim_method, c("sgd", "adam", "rmsprop"))) stop("Invalid Parameters.")
    if (optim_method == "sgd") { iter <- 10; lr <- 0.01; params <- paste0("method=", optim_method, "_", "lr=", lr, "_", "iter=", iter)} 
    if (optim_method == "adam") { iter <- 15; lr <- 0.02; params <- paste0("method=", optim_method, "_", "lr=", lr, "_", "iter=", iter)} 
    if (optim_method == "rmsprop") { iter <- 20; lr <- 0.03; params <- paste0("method=", optim_method, "_", "lr=", lr, "_", "iter=", iter)} 
    
    params <- paste0("method=", optim_method, "_", "lr=", lr, "_", "iter=", iter)
    
    out <- list(iter = iter, lr = lr, params = params)
    
    return (out)
}



NNtrain <- function(x, y, hidden_neur, optim_method) {
    
    hyper_params <- hyperParams(optim_method)
    
    iter <- hyper_params$iter
    lr <- hyper_params$lr
    
    NNreg <- neuralnetwork(X = x, y = y, 
                           val.prop = 0, 
                           standardize = FALSE, 
                           hidden.layers = c(hidden_neur), 
                           regression = TRUE,
                           loss.type = "squared",
                           n.epochs = iter,
                           optim.type = optim_method,
                           learn.rates = lr,
                           verbose = FALSE,
                           random.seed = as.integer(runif(1)*10000000))
    
    return (NNreg)
}
```


## Main Loop
```{r fig.height=5, fig.width=14}
for (dset in names(NNdatasets)) {

    ## =============================================
    ## EXTRACT INFORMATION FROM THE SELECTED DATASET
    ## =============================================
    ds     <- NNdatasets[[dset]]$ds
    Z      <- NNdatasets[[dset]]$Z
    neur   <- NNdatasets[[dset]]$neur
    nparNN <- NNdatasets[[dset]]$nparNN
    fmlaNN <- NNdatasets[[dset]]$fmlaNN
    donotremove  <- c("dset", "dsets", "ds", "Z", "neur", "TF", "nrep", "timer",
                      "donotremove", "donotremove2")
    donotremove2 <- c("dset", "dsets") 



    ## ===================================================
    ## SELECT THE FORMAT REQUIRED BY THE PACKAGE/ALGORITHM
    ## d = data.frame, m = matrix, v = vector/numeric
    ## ATTACH THE OBJECTS CREATED (x, y, Zxy, ... )
    ## ===================================================
    ZZ     <- prepareZZ(Z, xdmv = "d", ydmv = "v", zdm = "d", scale = TRUE)
    attach(ZZ)

    ## =============================================
    ## SELECT THE PACKAGE USED FOR TRAINING
    ## nrep => SELECT THE NUMBER OF INDEPENDANT RUNS
    ## iter => SELECT THE MAX NUMBER OF ITERATIONS
    ## TF   => PLOT THE RESULTS
    ## =============================================

    
    nrep   <- 5
    TF     <- TRUE 

    method <- c("sgd", "adam", "rmsprop")
        
    for (m in method) {
        
        descr  <- paste(dset, "ANN2::neuralnetwork", m, sep = "_")

        ## AUTO
        Ypred  <- list()
        Rmse   <- numeric(length = nrep)
        Mae    <- numeric(length = nrep)
    
        for(i in 1:nrep){
            event      <- paste0(descr, sprintf("_%.2d", i))
            timer$start(event)
            #### ADJUST THE FOLLOWING LINES TO THE PACKAGE::ALGORITHM
            
            hyper_params <- hyperParams(optim_method = m)

            NNreg      <- tryCatch(
                            NNtrain(x = x, y = y, hidden_neur = neur, optim_method = m),
                            error = function(y) {lm(y ~ 0, data = Zxy)}
                          )     
            y_pred     <- tryCatch(
                            ym0 + ysd0*predict(NNreg, x)$predictions,
                            error = ym0
                          )     
            ####
            Ypred[[i]] <- y_pred
            Rmse[i]    <- funRMSE(y_pred, y0)
            Mae[i]     <- funMAE(y_pred, y0)
            timer$stop(event, RMSE = Rmse[i], MAE = Mae[i], params = hyper_params$params, printmsg = FALSE)
        }
        best <- which(Rmse == min(Rmse, na.rm = TRUE))[1]
        best ; Rmse[[best]]
        
        ## ================================================
        ## PLOT ALL MODELS AND THE MODEL WITH THE BEST RMSE
        ## par OPTIONS CAN BE IMPROVED FOR A BETTER DISPLAY
        ## ================================================
        op <- par(mfcol = c(1,2))
        plotNN(xory, y0, uni, TF, main = descr)
        for (i in 1:nrep) lipoNN(xory, Ypred[[i]], uni, TF, col = i, lwd = 1)
        
        plotNN(xory, y0, uni, TF, main = descr)
        lipoNN(xory, Ypred[[best]], uni, TF, col = 4, lwd = 4)
        par(op)
    }


## ===========================
## DETACH ZZ - END OF THE LOOP
## ===========================
    detach(ZZ)
}
```


## Results

```{r}
dfr0 <- getTimer(timer) 

dfr  <- data.frame(
    ds_pkg.fun_algo = stringr::str_sub(dfr0[ ,1], 1, -4),
    run     = stringr::str_sub(dfr0[ ,1], -2, -1),
    dataset = stringr::str_replace_all(stringr::str_extract(dfr0[, 1], pattern = "^\\w*_"), fixed("_"), ""),
    method = stringr::str_replace_all(stringr::str_extract(dfr0[, 1], pattern = "_\\w*_"), fixed("_"), ""),
    Elapsed = round(dfr0[ ,4], 5),
    params = dfr0$params,
    dfr0[, c("RMSE","MAE")]
)


dfr
```

## Best Results

```{r}
dfr %>%
    group_by(dataset, method) %>%
    summarise(minRMSE = min(RMSE), meanRMSE = mean(RMSE), meanTime = mean(Elapsed)) %>%
    kable() %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
    collapse_rows(columns = 1:2, valign = "top")
```

```{r}
clearNN(donotremove)
```

